This is like building several very smart weather calculators that estimate how much water crops are actually losing to the air, then carefully tuning all the dials on those calculators so they give the most accurate answers possible.
Accurate, low-cost estimation of actual evapotranspiration (how much water crops really use) so farmers and water managers can optimize irrigation without relying solely on expensive or sparse field measurements.
Access to high-quality local climate/soil/crop datasets and well-calibrated models for specific regions or irrigation schemes can become a defensible data and workflow moat.
Classical-ML (Scikit/XGBoost)
Structured SQL
Medium (Integration logic)
Data availability and quality for ground-truth actual evapotranspiration measurements and local feature engineering; potential overfitting to specific climatic/soil conditions.
Early Majority
Focus on rigorous hyperparameter optimization for traditional ML models specifically targeting actual evapotranspiration prediction, improving accuracy over naïve or default-parameter baselines commonly used in agricultural decision-support tools.